Genetic Programming Bibliography entries for Jason H Moore

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GP coauthors/coeditors: Christian Darabos, Mario Giacobini, Ting Hu, Margaret J Eppstein, Joshua L Payne, Bill C White, James A Foster, Casey S Greene, Jeff Kiralis, Douglas P Hill, L H Heimisdottir, B M Lin, H Cho, Alena Orlenko, A A Ribeiro, A Simon-Soro, J Roach, D Shungin, J Ginnis, M A Simancas-Pallares, H D Spangler, A G Ferreira Zandona, J T Wright, P Ramamoorthy, H Koo, D Wu, K Divaris, Jose Guadalupe Hernandez, Anil Kumar Saini, Wolfgang Banzhaf, Maarten Keijzer, Mike Cattolico, Dirk V Arnold, Vladan Babovic, Christian Blum, Peter A N Bosman, Martin V Butz, Carlos Artemio Coello Coello, Dipankar Dasgupta, Sevan G Ficici, Arturo Hernandez-Aguirre, Gregory S Hornby, Hod Lipson, Phil McMinn, Gunther R Raidl, Franz Rothlauf, Conor Ryan, Dirk Thierens, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H Holmes, Daniel Howard, James Kennedy, Sanjeev Kumar, Fernando G Lobo, Julian F Miller, Frank Neumann, Martin Pelikan, Jordan B Pollack, Kumara Sastry, Kenneth O Stanley, Adrian Stoica, El-Ghazali Talbi, Ingo Wegener, Rachit Kumar, Joseph Romano, Marylyn D Ritchie, William La Cava, Sara Silva, Leonardo Vanneschi, Lee Spector, Tilak Raj Singh, James Taggart, Srinivas Suri, Thomas Helmuth, Kourosh Danai, Patryk Orzechowski, Bogdan Burlacu, Fabricio Olivetti de Franca, Marco Virgolin, Ying Jin, Michael Kommenda, Paul C Lee, Imran Ajmal, Xiruo Ding, Priyanka Solanki, Jordana B Cohen, Daniel S Herman, Trang T Le, Weixuan Fu, Elisabetta Manduchi, Joseph D Romano, Stefano Ruberto, Nicholas Matsumoto, Pedro Ribeiro Mendes Junior, Hyunjun Choi, Leo-Pekka Lyytikainen, Jari O Laurikka, Terho Lehtimaki, Sandra Batista, Joel S Parker, Lance W Hahn, Nancy J Olsen, Thomas M Aune, Nate Barney, Chia-Ti Tsai, Fu-Tien Chiang, Jiang Gui, Peter C Andrews, Jonathan M Fisher, Nicole A Lavender, La Creis Renee Kidd, Arvis Sulovari, Andrew J Saykin, Li Shen, Maksim Shestov, Peter Schmitt, Randal S Olson, Moshe Sipper, Yong Chen, R S Olson, Y Chen, Pedro Henrique Ribeiro, Ryan J Urbanowicz, Nathan Bartley, Sharon Tartarone, Steven Vitale, Junmei Cairns, Pedro J Caraballo, Richard M Weinshilboum, Liewei Wang, Matthew K Breitenstein, Daniel Kofink, Kjell Nikus, Pashupati Mishra, Pekka Kuukasjarvi, Pekka J Karhunen, Mika Kaehoenen, Folkert W Asselbergs, Franciszek Magiera, Pawel Renc, Arkadiusz Sitek, Jaroslaw Was, Joost Wagenaar, Kristine A Pattin, Thomas Caldwell, William Michael Rand, Alexandru-Adrian Tantar, Emilia Tantar, Nikhil Padhye, Aaron Baughman, Stefan Van Der Stock, Michael Perlitz, Steven M Gustafson, Jonathan Jesneck, Gisele L Pappa, John R Woodward, Matthew R Hyde, Jerry Swan, Stefan Wagner, Michael Affenzeller, Anne Auger, Alexandre Chotard, Verena Heidrich-Meisner, Olaf Mersmann, Petr Posik, Mike Preuss, Forrest Stonedahl, Rick L Riolo, Stephane Doncieux, Yaochu Jin, Jean-Baptiste Mouret, Daniele Loiacono, Albert Orriols-Puig, Kent McClymont, Ed Keedwell, Richard Everson, Jonathan E Fieldsend, David Walker, Francisco Fernandez de Vega, Carlos Cotta, Ekaterina (Katya) Vladislavleva, Stephen L Smith, Stefano Cagnoni, Robert M Patton, Sherri Goings, Alison A Motsinger, Katya Rodriguez-Vazquez, Gabriela Ochoa, David M Reif, Jay Moran, Miguel E Hernandez, Mark Kotanchek, Christopher S Coffey, William S Bush, Liang Mei, Jonathan Senn, Holly M Mortensen, Valerio Terragni, Karuna Ahuja, Andrew Sohn, Terence Soule, David A Pelta, Christine Solnon, Alan Dorin, Yew-Soon Ong, Dario Landa-Silva, Tina Yu, Aniko Ekart, Will N Browne, Tim Kovacs, Man Leung Wong, Clara Pizzuti, Jonathan E Rowe, Tobias Friedrich, Giovanni Squillero, Nicolas Bredeche, Jose A Lozano, Silja Meyer-Nieberg, Christian Igel, Rene Doursat, Gustavo Olague, Shin Yoo, John A Clark, Daniel R Tauritz, Jurgen Branke, Hans-Georg Beyer, Josh C Bongard, Dave Cliff, Riccardo Poli, Thomas Stuetzle, Richard A Watson, Anna L Tyler, Richard Cowper-Sal-lari, Benjamin Yang, Joshua C Gilbert,

Genetic Programming Articles by Jason H Moore

  1. Jason H. Moore. Is the evolution metaphor still necessary or even useful for genetic programming?. Genetic Programming and Evolvable Machines, 24(2):Article number: 21, 2023. Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection. details

  2. Joseph D. Romano and Liang Mei and Jonathan Senn and Jason H. Moore and Holly M. Mortensen. Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science. Computational Toxicology, 25:100261, 2023. details

  3. William G. La Cava and Paul C. Lee and Imran Ajmal and Xiruo Ding and Priyanka Solanki and Jordana B. Cohen and Jason H. Moore and Daniel S. Herman. A flexible symbolic regression method for constructing interpretable clinical prediction models. npj Digital Medicine, 6:Article number: 107, 2023. Silver 2023 HUMIES. details

  4. Elisabetta Manduchi and Trang Le and Weixuan Fu and Jason H Moore. Genetic analysis of coronary artery disease using tree-based automated machine learning informed by biology-based feature selection. IEEE/ACM transactions on computational biology and bioinformatics, 19(3):1379-1386, 2022. details

  5. Moshe Sipper and Jason H. Moore. AddGBoost: A gradient boosting-style algorithm based on strong learners. Machine Learning with Applications, 7:100243, 2022. details

  6. Moshe Sipper and Jason H. Moore. Symbolic-regression boosting. Genetic Programming and Evolvable Machines, 22(3):357-381, 2021. details

  7. Moshe Sipper and Jason H. Moore. Conservation machine learning: a case study of random forests. Scientific Reports, 11:Article number: 3629, 2021. details

  8. Stefano Ruberto and Valerio Terragni and Jason H. Moore. A semantic genetic programming framework based on dynamic targets. Genetic Programming and Evolvable Machines, 22(4):463-493, 2021. Special Issue: Highlights of Genetic Programming 2020 Events. details

  9. Joseph D. Romano and Trang T. Le and Weixuan Fu and Jason H. Moore. TPOT-NN: augmenting tree‑based automated machine learning with neural network estimators. Genetic Programming and Evolvable Machines, 22(2):207-227, 2021. details

  10. L. H. Heimisdottir and B. M. Lin and H. Cho and A. Orlenko and A. A. Ribeiro and A. Simon-Soro and J. Roach and D. Shungin and J. Ginnis and M. A. Simancas-Pallares and H. D. Spangler and A. G. Ferreira Zandona and J. T. Wright and P. Ramamoorthy and J. H. Moore and H. Koo and D. Wu and K. Divaris. Metabolomics Insights in Early Childhood Caries. Journal of Dental Research, 2021. Epub ahead of print. details

  11. Moshe Sipper and Jason H. Moore. Genetic Programming Theory and Practice: A Fifteen-Year Trajectory. Genetic Programming and Evolvable Machines, 21(1-2):169-179, 2020. Twentieth Anniversary Issue. details

  12. Alena Orlenko and Daniel Kofink and Leo-Pekka Lyytikainen and Kjell Nikus and Pashupati Mishra and Pekka Kuukasjarvi and Pekka J Karhunen and Mika Kaehoenen and Jari O Laurikka and Terho Lehtimaki and Folkert W Asselbergs and Jason H Moore. Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning. Bioinformatics, 36(6):1772-1778, 2020. details

  13. J. H. Moore and R. S. Olson and P. Schmitt and Y. Chen and E. Manduchi. How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases. Artificial Life, 26(1):23-37, 2020. details

  14. Trang T. Le and Weixuan Fu and Jason H. Moore. Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics, 36(1):250-256, 2020. details

  15. William La Cava and Jason H. Moore. Learning feature spaces for regression with genetic programming. Genetic Programming and Evolvable Machines, 21(3):433-467, 2020. Special Issue: Highlights of Genetic Programming 2019 Events. details

  16. Elisabetta Manduchi and Weixuan Fu and Joseph D. Romano and Stefano Ruberto and Jason H. Moore. Embedding covariate adjustments in tree-based automated machine learning for biomedical big data analyses. BMC Bioinformatics, 21:Article number: 430, 2020. details

  17. Jason H. Moore and Randal S. Olson and Yong Chen and Moshe Sipper. Automated discovery of test statistics using genetic programming. Genetic Programming and Evolvable Machines, 20(1):127-137, 2019. details

  18. William La Cava and Thomas Helmuth and Lee Spector and Jason H. Moore. A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection. Evolutionary Computation, 27(3):377-402, 2019. details

  19. William La Cava and Sara Silva and Kourosh Danai and Lee Spector and Leonardo Vanneschi and Jason H. Moore. Multidimensional genetic programming for multiclass classification. Swarm and Evolutionary Computation, 44:260-272, 2019. details

  20. Moshe Sipper and Weixuan Fu and Karuna Ahuja and Jason H. Moore. Investigating the parameter space of evolutionary algorithms. BioData Mining, 11(1) 2018. details

  21. Ting Hu and Wolfgang Banzhaf and Jason H. Moore. The effects of recombination on phenotypic exploration and robustness in evolution. Artificial Life, 20(4):457-470, 2014. Ten thousandth GP entry in the genetic programming bibliography. details

  22. Ting Hu and Joshua Payne and Wolfgang Banzhaf and Jason Moore. Evolutionary dynamics on multiple scales: a quantitative analysis of the interplay between genotype, phenotype, and fitness in linear genetic programming. Genetic Programming and Evolvable Machines, 13(3):305-337, 2012. Special issue on selected papers from the 2011 European conference on genetic programming. details

  23. Casey S. Greene and Jason H. Moore. Solving complex problems in human genetics using GP: challenges and opportunities. SIGEVOlution, 3(2):2-8, 2008. details

  24. Marylyn D. Ritchie and Alison A. Motsinger and William S. Bush and Christopher S. Coffey and Jason H. Moore. Genetic programming neural networks: A powerful bioinformatics tool for human genetics. Applied Soft Computing, 7(1):471-479, 2007. details

  25. Jason H. Moore and Nate Barney and Chia-Ti Tsai and Fu-Tien Chiang and Jiang Gui and Bill C. White. Symbolic Modeling of Epistasis. Human Heredity, 63(2):120-133, 2007. details

  26. James A. Foster and Jason H. Moore. GECCO-2006 Highlights: Biological Applications. SIGEVOlution, 1(3):23, 2006. details

  27. David M. Reif and Bill C. White and Jason H. Moore. Integrated analysis of genetic, genomic, and proteomic data. Expert Review of Proteomics, 1(1):67-75, 2004. details

  28. Jason H. Moore and Lance W. Hahn. Evaluation of a discrete dynamic systems approach for modeling the hierarchical relationship between genes, biochemistry, and disease susceptibility. Discrete and Continuous Dynamical Systems: Series B, 4(1):275-287, 2004. details

  29. Jason H. Moore and Lance W. Hahn. Petri net modeling of high-order genetic systems using grammatical evolution. BioSystems, 72(1-2):177-186, 2003. details

  30. Marylyn D. Ritchie and Bill C. White and Joel S. Parker and Lance W. Hahn and Jason H. Moore. Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics, 4(28) 2003. details

  31. Jason H. Moore and Joel S. Parker and Nancy J. Olsen and Thomas M. Aune. Symbolic Discriminant Analysis of Microarray Data in Automimmune Disease. Genetic Epidemiology, 23:57-69, 2002. details

Genetic Programming Conference proceedings edited by Jason H Moore

  1. Rick Riolo and Jason H. Moore and Mark Kotanchek editors, Genetic Programming Theory and Practice XI. Ann Arbor, USA, Springer, 2013. details

  2. Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb editors, GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference. Philadelphia, Pennsylvania, USA, 2012. details

  3. Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore editors, Genetic Programming Theory and Practice X. Ann Arbor, USA, Springer, 2012. details

  4. Bill Rand and Alexandru-Adrian Tantar and Emilia Tantar and Peter A. N. Bosman and Nikhil Padhye and Aaron Baughman and Stefan Van Der Stock and Michael Perlitz and Steven M. Gustafson and Jonathan Jesneck and Gisele L. Pappa and John Woodward and Matthew R. Hyde and Jerry Swan and Stefan Wagner and Michael Affenzeller and Anne Auger and Alexandre Chotard and Nikolaus Hansen and Verena Heidrich-Meisner and Olaf Mersmann and Petr Posik and Mike Preuss and Forrest Stonedahl and Rick Riolo and Stephane Doncieux and Yaochu Jin and Jean-Baptiste Mouret and Daniele Loiacono and Albert Orriols-Puig and Ryan Urbanowicz and Kent McClymont and Ed Keedwell and Richard Everson and Jonathan Fieldsend and David Walker and F. Fernandez de Vega and Carlos Cotta and Steven Gustafson and Ekaterina Vladislavleva and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and Sherri Goings and Alison Motsinger-Reif and Katya Rodriguez and Christian Blum and Gabriela Ochoa and Jason H. Moore editors, GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion. Philadelphia, Pennsylvania, USA, 2012. details

  5. Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore editors, Genetic Programming Theory and Practice IX. Ann Arbor, USA, Springer, 2011. details

  6. Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener editors, GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation. Atlanta, GA, USA, ACM, 2008. details

  7. Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener editors, GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation. London, UK, ACM Press, 2007. details

  8. Maarten Keijzer and Mike Cattolico and Dirk Arnold and Vladan Babovic and Christian Blum and Peter Bosman and Martin V. Butz and Carlos Coello Coello and Dipankar Dasgupta and Sevan G. Ficici and James Foster and Arturo Hernandez-Aguirre and Greg Hornby and Hod Lipson and Phil McMinn and Jason Moore and Guenther Raidl and Franz Rothlauf and Conor Ryan and Dirk Thierens editors, GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation. Seattle, Washington, USA, ACM Press, 2006. details

Genetic Programming conference papers by Jason H Moore

  1. Jose Guadalupe Hernandez and Anil Kumar Saini and Jason H Moore. Lexidate: Model Evaluation and Selection with Lexicase. In Jean-Baptiste Mouret and Kai Qin editors, Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, pages 279-282, Melbourne, Australia, 2024. Association for Computing Machinery. details

  2. Rachit Kumar and Joseph Romano and Marylyn Ritchie and Jason Moore. Extending Tree-Based Automated Machine Learning to Biomedical Image and Text Data Using Custom Feature Extractors. In Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen editors, Proceedings of the 2023 Genetic and Evolutionary Computation Conference, pages 599-602, Lisbon, Portugal, 2023. Association for Computing Machinery. details

  3. Pedro Ribeiro and Anil Saini and Jay Moran and Nicholas Matsumoto and Hyunjun Choi and Miguel Hernandez and Jason H. Moore. TPOT2: A New Graph-Based Implementation of the Tree-Based Pipeline Optimization Tool for Automated Machine Learning. In Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu editors, Genetic Programming Theory and Practice XX, pages 1-17, Michigan State University, USA, 2023. Springer. details

  4. Nicholas Matsumoto and Anil Kumar Saini and Pedro Ribeiro and Hyunjun Choi and Alena Orlenko and Leo-Pekka Lyytikainen and Jari O. Laurikka and Terho Lehtimaki and Sandra Batista and Jason H. Moore. Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning. In Gisele Pappa and Mario Giacobini and Zdenek Vasicek editors, EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming, volume 13986, pages 165-181, Brno, Czech Republic, 2023. Springer Verlag. details

  5. Patryk Orzechowski and Pawel Renc and William La Cava and Jason Moore and Arkadiusz Sitek and Jaroslaw Was and Joost Wagenaar. A Comparative Study of GP-based and State-of-the-art Classifiers on a Synthetic Machine Learning Benchmark. In Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco Fernandez de Vega and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio Della Cioppa and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William La Cava and Gabriel Kronberger and Steven Gustafson editors, Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion, pages 276-279, Boston, USA, 2022. Association for Computing Machinery. details

  6. Jason Moore. Genetic programming as an innovation engine for automated machine learning. In Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf editors, Genetic Programming Theory and Practice XIX, Ann Arbor, USA, 2022. details

  7. Stefano Ruberto and Valerio Terragni and Jason Moore. Towards Effective GP Multi-Class Classification Based on Dynamic Targets. In Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton editors, Proceedings of the 2021 Genetic and Evolutionary Computation Conference, pages 812-821, internet, 2021. Association for Computing Machinery. Nominated for best paper. details

  8. William La Cava and Patryk Orzechowski and Bogdan Burlacu and Fabricio de Franca and Marco Virgolin and Ying Jin and Michael Kommenda and Jason Moore. Contemporary Symbolic Regression Methods and their Relative Performance. In Joaquin Vanschoren and Sai-Kit Yeung editors, Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, volume 1, 2021. Curran. details

  9. Stefano Ruberto and Valerio Terragni and Jason H. Moore. Image Feature Learning with Genetic Programming. In Thomas Baeck and Mike Preuss and Andre Deutz and Hao Wang2 and Carola Doerr and Michael Emmerich and Heike Trautmann editors, 16th International Conference on Parallel Problem Solving from Nature, Part II, volume 12270, pages 63-78, Leiden, Holland, 2020. Springer. details

  10. Stefano Ruberto and Valerio Terragni and Jason H. Moore. Image Feature Learning with a Genetic Programming Autoencoder. In Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William La Cava and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe De Falco and Antonio Della Cioppa and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik editors, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 245-246, internet, 2020. Association for Computing Machinery. details

  11. Stefano Ruberto and Valerio Terragni and Jason H. Moore. SGP-DT: Towards Effective Symbolic Regression with a Semantic GP Approach Based on Dynamic Targets. In Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William La Cava and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe De Falco and Antonio Della Cioppa and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik editors, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 25-26, internet, 2020. Association for Computing Machinery. details

  12. Stefano Ruberto and Valerio Terragni and Jason H. Moore. SGP-DT: Semantic Genetic Programming Based on Dynamic Targets. In Ting Hu and Nuno Lourenco and Eric Medvet editors, EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming, volume 12101, pages 167-183, Seville, Spain, 2020. Springer Verlag. details

  13. Patryk Orzechowski and Franciszek Magiera and Jason Moore. Benchmarking manifold learning methods on a large collection of datasets. In Ting Hu and Nuno Lourenco and Eric Medvet editors, EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming, volume 12101, pages 135-150, Seville, Spain, 2020. Springer Verlag. details

  14. Trang T. Le and Weixuan Fu and Jason H. Moore. Large Scale Biomedical Data Analysis with Tree-Based Automated Machine Learning. In Richard Allmendinger and Hugo Terashima Marin and Efren Mezura Montes and Thomas Bartz-Beielstein and Bogdan Filipic and Ke Tang and David Howard and Emma Hart and Gusz Eiben and Tome Eftimov and William La Cava and Boris Naujoks and Pietro Oliveto and Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and Hisao Ishibuchi and Jonathan Fieldsend and Ozgur Akman and Khulood Alyahya and Juergen Branke and John R. Woodward and Daniel R. Tauritz and Marco Baioletti and Josu Ceberio Uribe and John McCall and Alfredo Milani and Stefan Wagner and Michael Affenzeller and Bradley Alexander and Alexander (Sandy) Brownlee and Saemundur O. Haraldsson and Markus Wagner and Nayat Sanchez-Pi and Luis Marti and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and Matthew Johns and Nick Ross and Ed Keedwell and Herman Mahmoud and David Walker and Anthony Stein and Masaya Nakata and David Paetzel and Neil Vaughan and Stephen Smith and Stefano Cagnoni and Robert M. Patton and Ivanoe De Falco and Antonio Della Cioppa and Umberto Scafuri and Ernesto Tarantino and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Erik Hemberg and Riyad Alshammari and Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and Ponnuthurai Nagaratnam and Roman Senkerik editors, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 21-22, internet, 2020. Association for Computing Machinery. details

  15. William La Cava and Jason H. Moore. Genetic Programming Approaches to Learning Fair Classifiers. In Carlos Artemio Coello Coello and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio Toscano Pulido and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco Fernandez de Vega and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann editors, Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pages 967-975, internet, 2020. Association for Computing Machinery. Best Paper. details

  16. Moshe Sipper and Jason H. Moore and Ryan J. Urbanowicz. New Pathways in Coevolutionary Computation. In Wolfgang Banzhaf and Erik Goodman and Leigh Sheneman and Leonardo Trujillo and Bill Worzel editors, Genetic Programming Theory and Practice XVII, pages 295-305, East Lansing, MI, USA, 2019. Springer. details

  17. Moshe Sipper and Jason Moore and Ryan Urbanowicz. Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions. In Lukas Sekanina and Ting Hu and Nuno Lourenco editors, EuroGP 2019: Proceedings of the 22nd European Conference on Genetic Programming, volume 11451, pages 146-161, Leipzig, Germany, 2019. Springer Verlag. details

  18. Patryk Orzechowski and Jason H. Moore. EBIC: a scalable biclustering method for large scale data analysis. In Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu Ceberio Uribe and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier Del Ser and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William La Cava and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 31-32, Prague, Czech Republic, 2019. ACM. details

  19. Jason H. Moore and Randal S. Olson and Yong Chen and Moshe Sipper. Discovering test statistics using genetic programming. In Richard Allmendinger and Carlos Cotta and Carola Doerr and Pietro S. Oliveto and Thomas Weise and Ales Zamuda and Anne Auger and Dimo Brockhoff and Nikolaus Hansen and Tea Tusar and Konstantinos Varelas and David Camacho-Fernandez and Massimiliano Vasile and Annalisa Riccardi and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Ozgur Akman and Khulood Alyahya and Juergen Branke and Jonathan Fieldsend and Tinkle Chugh and Jussi Hakanen and Josu Ceberio Uribe and Valentino Santucci and Marco Baioletti and John McCall and Emma Hart and Daniel R. Tauritz and John R. Woodward and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Eneko Osaba and Javier Del Ser and Pascal Kerschke and Boris Naujoks and Vanessa Volz and Anna I Esparcia-Alcazar and Riyad Alshammari and Erik Hemberg and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and David Walker and Matt Johns and Nick Ross and Ed Keedwell and Masaya Nakata and Anthony Stein and Takato Tatsumi and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen Smith and Stefano Cagnoni and Robert M. Patton and William La Cava and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and Richard Everson and Handing Wang and Yaochu Jin and Marcus Gallagher and Mike Preuss and Olivier Teytaud and Fernando Lezama and Joao Soares and Zita Vale editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 29-30, Prague, Czech Republic, 2019. ACM. details

  20. William La Cava and Tilak Raj Singh and James Taggart and Srinivas Suri and Jason H. Moore. Learning concise representations for regression by evolving networks of tree. In Tara Sainath editor, 7th International Conference on Learning Representations, ICLR 2019, New Orleans, Louisiana, USA, 2019. details

  21. William La Cava and Jason H. Moore. Semantic variation operators for multidimensional genetic programming. In Manuel Lopez-Ibanez and Thomas Stuetzle and Anne Auger and Petr Posik and Leslie Peprez Caceres and Andrew M. Sutton and Nadarajen Veerapen and Christine Solnon and Andries Engelbrecht and Stephane Doncieux and Sebastian Risi and Penousal Machado and Vanessa Volz and Christian Blum and Francisco Chicano and Bing Xue and Jean-Baptiste Mouret and Arnaud Liefooghe and Jonathan Fieldsend and Jose Antonio Lozano and Dirk Arnold and Gabriela Ochoa and Tian-Li Yu and Holger Hoos and Yaochu Jin and Ting Hu and Miguel Nicolau and Robin Purshouse and Thomas Baeck and Justyna Petke and Giuliano Antoniol and Johannes Lengler and Per Kristian Lehre editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1056-1064, Prague, Czech Republic, 2019. ACM. details

  22. Patryk Orzechowski and William La Cava and Jason H. Moore. Where are we now?: a large benchmark study of recent symbolic regression methods. In Hernan Aguirre and Keiki Takadama and Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and Andrew M. Sutton and Satoshi Ono and Francisco Chicano and Shinichi Shirakawa and Zdenek Vasicek and Roderich Gross and Andries Engelbrecht and Emma Hart and Sebastian Risi and Ekart Aniko and Julian Togelius and Sebastien Verel and Christian Blum and Will Browne and Yusuke Nojima and Tea Tusar and Qingfu Zhang and Nikolaus Hansen and Jose Antonio Lozano and Dirk Thierens and Tian-Li Yu and Juergen Branke and Yaochu Jin and Sara Silva and Hitoshi Iba and Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and Federica Sarro and Giuliano Antoniol and Anne Auger and Per Kristian Lehre editors, GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1183-1190, Kyoto, Japan, 2018. ACM. details

  23. Alena Orlenko and Jason H. Moore and Patryk Orzechowski and Randal S. Olson and Junmei Cairns and Pedro J. Caraballo and Richard M. Weinshilboum and Liewei Wang and Matthew K. Breitenstein. Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure. In Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Marylyn D. Ritchie and Tiffany Murray and Teri E. Klein editors, Pacific Symposium on Biocomputing, pages 460-471, Hawaii, USA, 2018. World Scientific. details

  24. Jason H. Moore and Maksim Shestov and Peter Schmitt and Randal S. Olson. A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods. In Russ B. Altman and A. Keith Dunker and Lawrence Hunter and Marylyn D. Ritchie and Tiffany Murray and Teri E. Klein editors, Pacific Symposium on Biocomputing, pages 259-267, Hawaii, USA, 2018. details

  25. William La Cava and Sara Silva and Kourosh Danai and Lee Spector and Leonardo Vanneschi and Jason H. Moore. A multidimensional genetic programming approach for identifying epsistatic gene interactions. In Carlos Cotta and Tapabrata Ray and Hisao Ishibuchi and Shigeru Obayashi and Bogdan Filipic and Thomas Bartz-Beielstein and Grant Dick and Masaharu Munetomo and Silvino Fernandez Alzueta and Thomas Stuetzle and Pablo Valledor Pellicer and Manuel Lopez-Ibanez and Daniel R. Tauritz and Pietro S. Oliveto and Thomas Weise and Borys Wrobel and Ales Zamuda and Anne Auger and Julien Bect and Dimo Brockhoff and Nikolaus Hansen and Rodolphe Le Riche and Victor Picheny and Bilel Derbel and Ke Li and Hui Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Stephane Doncieux and Richard Duro and Joshua Auerbach and Harold de Vladar and Antonio J. Fernandez-Leiva and JJ Merelo and Pedro A. Castillo-Valdivieso and David Camacho-Fernandez and Francisco Chavez de la O and Ozgur Akman and Khulood Alyahya and Juergen Branke and Kevin Doherty and Jonathan Fieldsend and Giuseppe Carlo Marano and Nikos D. Lagaros and Koichi Nakayama and Chika Oshima and Stefan Wagner and Michael Affenzeller and Boris Naujoks and Vanessa Volz and Tea Tusar and Pascal Kerschke and Riyad Alshammari and Tokunbo Makanju and Brad Alexander and Saemundur O. Haraldsson and Markus Wagner and John R. Woodward and Shin Yoo and John McCall and Nayat Sanchez-Pi and Luis Marti and Danilo Vasconcellos and Masaya Nakata and Anthony Stein and Nadarajen Veerapen and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Stephen L. Smith and Stefano Cagnoni and Robert M. Patton and William La Cava and Randal Olson and Patryk Orzechowski and Ryan Urbanowicz and Ivanoe De Falco and Antonio Della Cioppa and Ernesto Tarantino and Umberto Scafuri and P. G. M. Baltus and Giovanni Iacca and Ahmed Hallawa and Anil Yaman and Alma Rahat and Handing Wang and Yaochu Jin and David Walker and Richard Everson and Akira Oyama and Koji Shimoyama and Hemant Kumar and Kazuhisa Chiba and Pramudita Satria Palar editors, GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 23-24, Kyoto, Japan, 2018. ACM. details

  26. Ryan J. Urbanowicz and Ben Yang and Jason H. Moore. Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System. In Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo editors, Genetic Programming Theory and Practice XV, pages 55-71, University of Michigan in Ann Arbor, USA, 2017. Springer. details

  27. Andrew Sohn and Randal S. Olson and Jason H. Moore. Toward the Automated Analysis of Complex Diseases in Genome-wide Association Studies Using Genetic Programming. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 489-496, Berlin, Germany, 2017. ACM. details

  28. Randal S. Olson and Moshe Sipper and William La Cava and Sharon Tartarone and Steven Vitale and Weixuan Fu and Patryk Orzechowski and Ryan J. Urbanowicz and John H. Holmes and Jason H. Moore. A System for Accessible Artificial Intelligence. In Wolfgang Banzhaf and Randal S. Olson and William Tozier and Rick Riolo editors, Genetic Programming Theory and Practice XV, pages 121-134, University of Michigan in Ann Arbor, USA, 2017. Springer. details

  29. William La Cava and Sara Silva and Leonardo Vanneschi and Lee Spector and Jason Moore. Genetic Programming Representations for Multi-dimensional Feature Learning in Biomedical Classification. In Giovanni Squillero editor, 20th European Conference on the Applications of Evolutionary Computation, volume 10199, pages 158-173, Amsterdam, 2017. Springer. details

  30. William La Cava and Jason H. Moore. Ensemble Representation Learning: An Analysis of Fitness and Survival for Wrapper-based Genetic Programming Methods. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 961-968, Berlin, Germany, 2017. ACM. details

  31. William La Cava and Jason Moore. A General Feature Engineering Wrapper for Machine Learning Using epsilon-Lexicase Survival. In Mauro Castelli and James McDermott and Lukas Sekanina editors, EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming, volume 10196, pages 80-95, Amsterdam, 2017. Springer Verlag. details

  32. Randal S. Olson and Jason H. Moore. TPOT: A Tree-based Pipeline Optimization Tool for Automating Data Science. In Frank Hutter and Lars Kotthoff and Joaquin Vanschoren editors, AutoML 2016 workshop, New York City, USA, 2016. Collocated with ICML 2016. details

  33. Randal S. Olson and Nathan Bartley and Ryan J. Urbanowicz and Jason H. Moore. Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. In Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing editors, GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation, pages 485-492, Denver, USA, 2016. ACM. details

  34. Randal S. Olson and Jason H. Moore. Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool. In Rick Riolo and Bill Worzel and Brian Goldman and Bill Tozier editors, Genetic Programming Theory and Practice XIV, pages 211-223, Ann Arbor, USA, 2016. Springer. details

  35. Randal S. Olson and Ryan J. Urbanowicz and Peter C. Andrews and Nicole A. Lavender and La Creis Kidd and Jason H. Moore. Automating Biomedical Data Science Through Tree-Based Pipeline Optimization. In Giovanni Squillero and Paolo Burelli editors, Proceedings of the 19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016, Part I, volume 9597, pages 123-137, Porto, Portugal, 2016. Springer. Best paper, EvoBio track. details

  36. Jason H. Moore and Casey S. Greene and Douglas P. Hill. Identification of Novel Genetic Models of Glaucoma Using the ``EMERGENT'' Genetic Programming-Based Artificial Intelligence System. In Rick Riolo and William P. Worzel and Mark Kotanchek editors, Genetic Programming Theory and Practice XII, pages 17-35, Ann Arbor, USA, 2014. Springer. details

  37. Ting Hu and Wolfgang Banzhaf and Jason Moore. Population Exploration on Genotype Networks in Genetic Programming. In Thomas Bartz-Beielstein and Juergen Branke and Bogdan Filipic and Jim Smith editors, 13th International Conference on Parallel Problem Solving from Nature, volume 8672, pages 424-333, Ljubljana, Slovenia, 2014. Springer. details

  38. Ting Hu and Wolfgang Banzhaf and Jason H. Moore. Robustness and Evolvability of Recombination in Linear Genetic Programming. In Krzysztof Krawiec and Alberto Moraglio and Ting Hu and A. Sima Uyar and Bin Hu editors, Proceedings of the 16th European Conference on Genetic Programming, EuroGP 2013, volume 7831, pages 97-108, Vienna, Austria, 2013. Springer Verlag. details

  39. Christian Darabos and Mario Giacobini and Ting Hu and Jason H. Moore. Levy-Flight Genetic Programming: Towards a New Mutation Paradigm. In Mario Giacobini and Leonardo Vanneschi and William S. Bush editors, 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, volume 7246, pages 38-49, Malaga, Spain, 2012. Springer Verlag. details

  40. Ting Hu and Joshua Payne and Jason Moore and Wolfgang Banzhaf. Robustness, Evolvability, and Accessibility in Linear Genetic Programming. In Sara Silva and James A. Foster and Miguel Nicolau and Mario Giacobini and Penousal Machado editors, Proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, volume 6621, pages 13-24, Turin, Italy, 2011. Springer Verlag. details

  41. Anna L. Tyler and Bill C. White and Casey S. Greene and Peter C. Andrews and Richard Cowper-Sal-lari and Jason H. Moore. Development and Evaluation of an Open-Ended Computational Evolution System for the Creation of Digital Organisms with Complex Genetic Architecture. In Andy Tyrrell editor, 2009 IEEE Congress on Evolutionary Computation, pages 2907-2912, Trondheim, Norway, 2009. IEEE Press. details

  42. Casey S. Greene and Bill C. White and Jason H. Moore. Sensible Initialization Using Expert Knowledge for Genome-Wide Analysis of Epistasis Using Genetic Programming. In Andy Tyrrell editor, 2009 IEEE Congress on Evolutionary Computation, pages 1289-1296, Trondheim, Norway, 2009. IEEE Press. details

  43. Casey S. Greene and Jeff Kiralis and Jason H. Moore. Nature-Inspired Algorithms for the Genetic Analysis of Epistasis in Common Human Diseases: Theoretical Assessment of Wrapper vs. Filter Approaches. In Andy Tyrrell editor, 2009 IEEE Congress on Evolutionary Computation, pages 800-807, Trondheim, Norway, 2009. IEEE Press. details

  44. Jason H. Moore and Peter C. Andrews and Nate Barney and Bill C. White. Development and Evaluation of an Open-Ended Computational Evolution System for the Genetic Analysis of Susceptibility to Common Human Diseases. In Elena Marchiori and Jason H. Moore editors, Proceedings of the 6th European Conference, on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2008, volume 4973, pages 129-140, Naples, Italy, 2008. Springer. details

  45. Ryan J. Urbanowicz and Nate Barney and Bill C. White and Jason H. Moore. Mask functions for the symbolic modeling of epistasis using genetic programming. In Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener editors, GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 339-346, Atlanta, GA, USA, 2008. ACM. details

  46. Casey S. Greene and Bill C. White and Jason H. Moore. Using expert knowledge in initialization for genome-wide analysis of epistasis using genetic programming. In Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener editors, GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 351-352, Atlanta, GA, USA, 2008. ACM. details

  47. Casey S. Greene and Bill C. White and Jason H. Moore. An Expert Knowledge-Guided Mutation Operator for Genome-Wide Genetic Analysis Using Genetic Programming. In Jagath C. Rajapakse and Bertil Schmidt and L. Gwenn Volkert editors, Proceedings of the second IAPR International Workshop Pattern Recognition in Bioinformatics, PRIB 2007, volume 4774, pages 30-40, Singapore, 2007. Springer. details

  48. Jason H. Moore and Nate Barney and Bill C. White. Towards human-human-computer interaction for biologically-inspired problem-solving in human genetics. In Dirk Thierens and Hans-Georg Beyer and Josh Bongard and Jurgen Branke and John Andrew Clark and Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and Julian F. Miller and Jason Moore and Frank Neumann and Martin Pelikan and Riccardo Poli and Kumara Sastry and Kenneth Owen Stanley and Thomas Stutzle and Richard A Watson and Ingo Wegener editors, GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, volume 1, pages 432-433, London, 2007. ACM Press. details

  49. Jason H. Moore and Bill C. White. Exploiting Expert Knowledge in Genetic Programming for Genome-Wide Genetic Analysis. In Thomas Philip Runarsson and Hans-Georg Beyer and Edmund Burke and Juan J. Merelo-Guervos and L. Darrell Whitley and Xin Yao editors, Parallel Problem Solving from Nature - PPSN IX, volume 4193, pages 969-977, Reykjavik, Iceland, 2006. Springer-Verlag. details

  50. Margaret J. Eppstein and Joshua L. Payne and Bill C. White and Jason H. Moore. Hill-climbing through "random chemistry" for detecting epistasis. In J\"orn Grahl editor, Late breaking paper at Genetic and Evolutionary Computation Conference (GECCO'2006), Seattle, WA, USA, 2006. details

  51. Bill C. White and Joshua C. Gilbert and David M. Reif and Jason H. Moore. A Statistical Comparison of Grammatical Evolution Strategies in the Domain of Human Genetics. In David Corne and Zbigniew Michalewicz and Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and Garrison Greenwood and Tan Kay Chen and Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and Jennifier Willies and Juan J. Merelo Guervos and Eugene Eberbach and Bob McKay and Alastair Channon and Ashutosh Tiwari and L. Gwenn Volkert and Dan Ashlock and Marc Schoenauer editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 1, pages 491-497, Edinburgh, UK, 2005. IEEE Press. details

  52. Bill C. White and Jason H. Moore. Systems biology thought experiments in human genetics using artificial life and grammatical evolution. In Jordan Pollack and Mark Bedau and Phil Husbands and Takashi Ikegami and Richard A. Watson editors, Artificial Life XI Ninth International Conference on the Simulation and Synthesis of Living Systems, pages 581-586, Boston, Massachusetts, 2004. The MIT Press. details

  53. Marylyn D. Ritchie and Christopher S. Coffey and Jason H. Moore. Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics. In Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell editors, Genetic and Evolutionary Computation -- GECCO-2004, Part I, volume 3102, pages 438-448, Seattle, WA, USA, 2004. Springer-Verlag. details

  54. Jason H. Moore and Lance W. Hahn. Systems Biology Modeling in Human Genetics Using Petri Nets and Grammatical Evolution. In Kalyanmoy Deb and Riccardo Poli and Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and Paul Darwen and Dipankar Dasgupta and Dario Floreano and James Foster and Mark Harman and Owen Holland and Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and Dirk Thierens and Andy Tyrrell editors, Genetic and Evolutionary Computation -- GECCO-2004, Part I, volume 3102, pages 392-401, Seattle, WA, USA, 2004. Springer-Verlag. details

  55. Jason Moore and Lance Hahn. An Improved Grammatical Evolution Strategy for Hierarchical Petri Net Modeling of Complex Genetic Systems. In Guenther R. Raidl and Stefano Cagnoni and Jurgen Branke and David W. Corne and Rolf Drechsler and Yaochu Jin and Colin R. Johnson and Penousal Machado and Elena Marchiori and Franz Rothlauf and George D. Smith and Giovanni Squillero editors, Applications of Evolutionary Computing, EvoWorkshops2004: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC, volume 3005, pages 63-72, Coimbra, Portugal, 2004. Springer Verlag. details

  56. Marylyn D. Ritchie and Bill C. White and Joel S. Parker and Lance W. Hahn and Jason H. Moore. Optimization of Neural Networks using Genetic Programming Improves Detection and Modeling of Gene-Gene Interactions in Studies of Human Diseases. In Bart Rylander editor, Genetic and Evolutionary Computation Conference Late Breaking Papers, pages 255-259, Chicago, USA, 2003. details

  57. Marylyn D. Ritchie and Bill C. White and Joel S. Parker and Lance W. Hahn and Jason H. Moore. Optimization of Neural Networks using Genetic Programming to Improve Detection and Modeling of Gene-Gene Interactions in Studies of Human Diseases. In Alwyn M. Barry editor, GECCO 2003: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pages 72-74, Chigaco, 2003. AAAI. details

  58. David M. Reif and Bill C. White and Nancy Olsen and Thomas Aune and Jason H. Moore. Complex Function Sets Improve Symbolic Discriminant Analysis of Microarray Data. In E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller editors, Genetic and Evolutionary Computation -- GECCO-2003, volume 2724, pages 2277-2287, Chicago, 2003. Springer-Verlag. details

  59. Jason H. Moore and Lance W. Hahn. Grammatical Evolution for the Discovery of Petri Net Models of Complex Genetic Systems. In E. Cant\'u-Paz and J. A. Foster and K. Deb and D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and R. Standish and G. Kendall and S. Wilson and M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and A. C. Schultz and K. Dowsland and N. Jonoska and J. Miller editors, Genetic and Evolutionary Computation -- GECCO-2003, volume 2724, pages 2412-2413, Chicago, 2003. Springer-Verlag. details

  60. Jason Moore. Cross Validation Consistency for the Assessment of Genetic Programming Results in Microarray Studies. In G\"unther R. Raidl and Stefano Cagnoni and Juan Jes\'us Romero Cardalda and David W. Corne and Jens Gottlieb and Agn\`es Guillot and Emma Hart and Colin G. Johnson and Elena Marchiori and Jean-Arcady Meyer and Martin Middendorf editors, Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM, volume 2611, pages 99-106, University of Essex, UK, 2003. Springer-Verlag. details

  61. Jason Moore and Joel Parker and Lance Hahn. Symbolic Discriminant Analysis for Mining Gene Expression Patterns. In Luc De Raedt and Peter Flach editors, 12th European Conference on Machine Learning (ECML'01), volume 2167, pages 372-381, Freiburg, Germany, 2001. Springer. details

Genetic Programming book chapters by Jason H Moore

  1. Jason H. Moore and Pedro H. Ribeiro and Nicholas Matsumoto and Anil K. Saini. Genetic Programming as an Innovation Engine for Automated Machine Learning: The Tree-Based Pipeline Optimization Tool (TPOT). In Wolfgang Banzhaf and Penousal Machado and Mengjie Zhang editors, Handbook of Evolutionary Machine Learning, pages 439-455. Springer Nature, Singapore, 2023. details

  2. Randal S. Olson and Jason H. Moore. TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning. In Frank Hutter and Lars Kotthoff and Joaquin Vanschoren editors, Automated Machine Learning: Methods, Systems, Challenges, chapter 8, pages 151-160. Springer, 2019. details

  3. Jason H. Moore and Moshe Sipper. Grammatical Evolution Strategies for Bioinformatics and Systems Genomics. In Conor Ryan and Michael O'Neill and J. J. Collins editors, Handbook of Grammatical Evolution, chapter 16, pages 395-405. Springer, 2018. details

  4. Jason H. Moore and Douglas P. Hill and Andrew Saykin and Li Shen. Exploring Interestingness in a Computational Evolution System for the Genome-Wide Genetic Analysis of Alzheimer's Disease. In Rick Riolo and Jason H. Moore and Mark Kotanchek editors, Genetic Programming Theory and Practice XI, chapter 2, pages 31-45. Springer, Ann Arbor, USA, 2013. details

  5. Jason H. Moore and Douglas P. Hill and Arvis Sulovari and LaCreis Kidd. Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing. In Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore editors, Genetic Programming Theory and Practice X, chapter 7, pages 87-101. Springer, Ann Arbor, USA, 2012. details

  6. Christian Darabos and Mario Giacobini and Ting Hu and Jason H. Moore. A New Mutation Paradigm for Genetic Programming. In Rick Riolo and Ekaterina Vladislavleva and Marylyn D. Ritchie and Jason H. Moore editors, Genetic Programming Theory and Practice X, chapter 4, pages 45-58. Springer, Ann Arbor, USA, 2012. details

  7. Jason H. Moore and Douglas P. Hill and Jonathan M. Fisher and Nicole Lavender and La Creis Kidd. Human-Computer Interaction in a Computational Evolution System for the Genetic Analysis of Cancer. In Rick Riolo and Ekaterina Vladislavleva and Jason H. Moore editors, Genetic Programming Theory and Practice IX, chapter 9, pages 153-171. Springer, Ann Arbor, USA, 2011. details

  8. Kristine A. Pattin and Joshua L. Payne and Douglas P. Hill and Thomas Caldwell and Jonathan M. Fisher and Jason H. Moore. Exploiting Expert Knowledge of Protein-Protein Interactions in a Computational Evolution System for Detecting Epistasis. In Rick Riolo and Trent McConaghy and Ekaterina Vladislavleva editors, Genetic Programming Theory and Practice VIII, volume 8 of Genetic and Evolutionary Computation, chapter 12, pages 195-210. Springer, Ann Arbor, USA, 2010. details

  9. Casey S. Greene and Douglas P. Hill and Jason H. Moore. Environmental Sensing of Expert Knowledge in a Computational Evolution System for Complex Problem Solving in Human Genetics. In Rick L. Riolo and Una-May O'Reilly and Trent McConaghy editors, Genetic Programming Theory and Practice VII, chapter 2, pages 19-36. Springer, Ann Arbor, 2009. details

  10. Jason H. Moore and Casey S. Greene and Peter C. Andrews and Bill C. White. Does Complexity Matter? Artificial Evolution, Computational Evolution and the Genetic Analysis of Epistasis in Common Human Diseases. In Rick L. Riolo and Terence Soule and Bill Worzel editors, Genetic Programming Theory and Practice VI, chapter 9, pages 125-145. Springer, Ann Arbor, 2008. details

  11. Jason H. Moore and Nate Barney and Bill C. White. Solving Complex Problems in Human Genetics Using Genetic Programming: The Importance of Theorist-Practitioner-computer Interaction. In Rick L. Riolo and Terence Soule and Bill Worzel editors, Genetic Programming Theory and Practice V, chapter 5, pages 69-86. Springer, Ann Arbor, 2007. details

  12. Jason H. Moore and Bill C. White. Genome-Wide Genetic Analysis Using Genetic Programming: The Critical Need for Expert Knowledge. In Rick L. Riolo and Terence Soule and Bill Worzel editors, Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, pages 11-28. Springer, Ann Arbor, 2006. details

  13. J. H. Moore and J. S. Parker. Evolutionary computation in microarray data analysis. In S. Lin and K. Johnson editors, Methods of Microarray Data Analysis, pages 23-35. Kluwer Academic Publishers, Boston, 2002. details

Genetic Programming other entries for Jason H Moore

  1. Jason Moore. Automated machine learning using genetic programming. 2021. details

  2. Moshe Sipper and Jason H. Moore. Programmatic Boosting. 2020. Apr 2023 Withdrawn, see instead \citeSipper_Moore:GPEM \citeSipper:2022:mlwa. details

  3. Moshe Sipper and Weixuan Fu and Karuna Ahuja and Jason H. Moore. From MEGATON to RASCAL: Surfing the Parameter Space of Evolutionary Algorithms. 2017. details

  4. Jason H. Moore and Joel S. Parker and Lance W. Hahn. Symbolic Discriminant Analysis for Mining Gene Expression Patterns. 2000. submitted abstract. details